期刊
CLINICAL BREAST CANCER
卷 21, 期 3, 页码 256-+出版社
CIG MEDIA GROUP, LP
DOI: 10.1016/j.clbc.2020.10.011
关键词
Breast cancer; Calcification; Contrast media; Dual energy; Positive predictive value
类别
资金
- National Key R&D Program of China [2017YFC1309101, 2017YFC1309104]
- Science Foundation of Peking University Cancer Hospital [2020-24]
- Beijing Municipal Science & Technology Commission [Z181100001918001]
- Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support [ZYLX201803]
- Beijing Million Talents Project [2017A13]
- Beijing Hospitals Authority' Ascent Plan [20191103]
This study investigated the potential of contrast-enhanced mammography (GEM) in improving the diagnostic accuracy of BI-RADS 4 calcification-only lesions, particularly for those with low-risk morphology and distribution. Results showed that GEM has the ability to aid in the diagnosis of these lesions, with significant improvements observed in positive predictive values and misdiagnosis rates when using contrast-enhanced images compared to low-energy images alone. Machine learning methods also showed enhanced diagnostic performance in both low-risk and high-risk groups.
This study aimed to test the possibility of contrast-enhanced mammography (GEM) in improving the diagnostic accuracy of Breast Imaging Reporting and Data System (BI-RADS) 4 calcification-only lesions. Seventy-four calcifications were included. The results indicated that GEM has the potential to aid in the diagnosis of BIRADS 4 calcification-only lesions; in particular, those presented as low risk in morphology and/or distribution may benefit more. Background: Contrast-enhanced mammography (CEM) is a novel breast imaging technique that can provide additional information of breast tissue blood supply. This study aimed to test the possibility of CEM in improving the diagnostic accuracy of Breast Imaging Reporting and Data System (BI-RADS) 4 calcification-only lesions with consideration of morphology and distribution. Patients and Methods: Data of patients with suspicious malignant calcification-only lesions (BI-RADS 4) on low-energy CEM and proved pathologic diagnoses were retrospectively collected. Two junior radiologists independently reviewed the two sets of CEM images, low-energy images (LE) to describe the calcifications by morphology and distribution type, and recombined images (CE) to record the presence of enhancement. Low-risk and high-risk groups were divided by calcification morphology, distribution, and both, respectively. Positive predictive values and misdiagnosis rates (MDR) were compared between LE-only reading and CE reading. Diagnostic performance was also tested using machine learning method. Results: The study included 74 lesions (26 malignant and 48 benign). Positive predictive values were significantly higher and MDRs were significantly lower using CE images than using LE alone for both the low-risk morphology type and low-risk distribution type (P < .05). MDRs were significantly lower when using CE images (18.18%-24.00%) than using LE images alone in low-risk group (76.36%-80.00%) (P < .05). Using a machine learning method, significant improvements in the area under the receiver operating characteristic curve were observed in both low-risk and high-risk groups. Conclusion: CEM has the potential to aid in the diagnosis of BI-RADS 4 calcification-only lesions; in particular, those presented as low risk in morphology and/or distribution may benefit more. (C) 2020 Elsevier Inc. All rights reserved.
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